Cluster analysis


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Related to Cluster analysis: factor analysis, Discriminant analysis

Cluster analysis

A statistical technique that identifies clusters of stocks whose returns are highly correlated within each cluster and relatively uncorrelated across clusters. Cluster analysis has identified groupings such as growth, cyclical, stable, and energy stocks.
References in periodicals archive ?
Cluster analysis. A concise guide to market research: The process, data, and methods using IBM SPSS Statistics (pp.
The estimated scale values and t-parameters were then analyzed by means of a cluster analysis (see, e.g., Hofmans & Mullet, 2013) by using the R package cluster (Kaufman & Rousseeuw, 1990; Maechler, Rousseeuw, Struyf, & Hubert, 2005).
The method that will be used in this research will be Cluster Analysis. The primary purpose of this research is to determine the socio-economic position of the Turkish women as compared with the European Union countries.
Keywords: Chickpea; Diversity; Correlation; Cluster analysis; Principal component analysis
By cluster analysis by Ward method, the populations of two varieties were completely differentiated.
To determine if whole-genome cluster analysis can improve subtype discrimination and cluster detection in the public health laboratory, we sequenced 93 S.
The current article is structured as follows: section 2 is dedicated to the descriptive presentation of the database we used and the way in which the analysis was conducted for both parts of the study (we also included the reasons for selecting these particular characteristics, and we also explained the method of quantification used for each characteristic); section 3 is dedicated to the results we obtained, both through sigma-convergence and cluster analysis (we also describe the clusters in terms of member states included in those clusters, as well as the characteristics that they have in common); section 4 is dedicated to the presentation of the overall conclusions and the implications of the results we reached during our research.
Cluster analysis and principal component analysis (PCA) are two other multivariate analyses that can be used to identify natural clustering pattern and group objects on the basis of similarities among the samples [24].
Besides the significance analysis, the cluster analysis is another class of analysis methods to uncover the useful information from gene expression data [5].
The correlation of the samples, through the cluster analysis, clusters the similar samples into a subclass, refining the sample to eliminate individual outlier samples, and divides all samples into some subclasses.
Summary: Principal components analysis and cluster analysis were used to investigate the properties of different corn varieties.
Accompanied by the continuous expansion of application areas and more in-depth of clustering analysis, in recent years high-dimensional clustering problem has gradually become the focus of the study of cluster analysis. With the rapid development of a variety of detectors and sensor technology, the number of spatial data properties also increased by a few dozens or even hundreds for spatial data.